scholarly journals Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3420
Author(s):  
Yi Liang ◽  
Haichao Wang
Keyword(s):  
Search Algorithm ◽  
Evaluation Model ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Sustainability Evaluation ◽  
Belt And Road ◽  
Improved Cuckoo Search Algorithm ◽  
Pair Analysis ◽  
The Belt And Road

Scientific and timely sustainability evaluation of the photovoltaic industry along the Belt and Road is of great significance. In this paper, a novel hybrid evaluation model is proposed for accurate and real-time assessment that integrates modified set pair analysis with least squares support vector machine that combines improved cuckoo search algorithm. First, the indicator system is set from five principles, namely economy, politics, society, ecological environment and resources. Then, the traditional approach is established through modifying set pair analysis on the basis of variable fuzzy set coupling evaluation theory. A modern intelligent assessment model is designed that integrates improved cuckoo search algorithm and least squares support vector machine where the concept of random weight is introduced in improved cuckoo search algorithm. In the case analysis, the relative errors calculated by the proposed model all fluctuate in the range of [−3%, 3%], indicating that it has the strongest fitting and learning ability. The empirical analysis verifies the scientificity and precision of the method and points out influencing factors. It provides a new idea for rapid and effective assessment of PV industry along the Belt and Road, as well as assistance for the sustainable development of this industry. This paper innovatively proposes the sustainability evaluation index system and evaluation model for the photovoltaic industry in countries along the Belt and Road, thus contributing to the promotion of sustainable development of the photovoltaic industry in countries along the Belt and Road.

scholarly journals An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

2014 ◽  
Vol 2 (6) ◽  
pp. 481-504 ◽  
Author(s):  
Xiangfei Li ◽  
Zaisheng Zhang ◽  
Chao Huang
Keyword(s):  
Empirical Mode Decomposition ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Stock Index ◽  
Support Vector ◽  
Mode Decomposition ◽  
Parameters Selection ◽  
Predicted Values ◽  
Improved Cuckoo Search Algorithm

AbstractIn order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms in parameters selection.

scholarly journals Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ziping He ◽  
Kewen Xia ◽  
Wenjia Niu ◽  
Nelofar Aslam ◽  
Jingzhong Hou
Keyword(s):  
Search Algorithm ◽  
Recognition Rate ◽  
Model Performance ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Coordination Mechanism ◽  
Support Vector ◽  
Local Optimum ◽  
Improved Cuckoo Search Algorithm

Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate’s nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

Coverage optimization of VLC in smart homes based on improved cuckoo search algorithm

2017 ◽  
Vol 116 ◽  
pp. 63-78 ◽  
Author(s):  
Geng Sun ◽  
Yanheng Liu ◽  
Ming Yang ◽  
Aimin Wang ◽  
Shuang Liang ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Smart Homes ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Coverage Optimization ◽  
Improved Cuckoo Search Algorithm

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

A solution method for treatment scheduling in rehabilitation hospitals with real-life requirements

2018 ◽  
Vol 30 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Liyang Xiao ◽  
Mahjoub Dridi ◽  
Amir Hajjam El Hassani ◽  
Wanlong Lin ◽  
Hongying Fei
Keyword(s):  
Waiting Time ◽  
Programming Model ◽  
Search Algorithm ◽  
Real Life ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Mixed Integer ◽  
Solution Quality ◽  
Improved Cuckoo Search Algorithm ◽  
Rehabilitation Hospitals

Abstract In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances.

Sign in / Sign up

Export Citation Format

Share Document